Multiple vehicles based new landmark feature mapping for highly autonomous driving map

A highly autonomous driving (HAD) map can improve the perception and localization performance of autonomous cars. However, when the HAD map cannot represent the real world precisely as a result of changes in the environment, reliability of autonomous cars may be decreased; therefore, it is essential that up-to-date map information is maintained. In order to keep the HAD map up-to-date, new landmark features must be updated continuously. This paper focuses on new landmark feature mapping in the HAD map based on multiple cars equipped with low-cost sensors. The features can be accurately mapped in the HAD map by matching between sensor information and the HAD map information, and by integrating multiple features estimated from various cars. The proposed algorithm is composed of three steps: data acquisition, new landmark feature map estimation using the HAD map, and feature integration. The algorithm is evaluated by means of certain simulation scenarios and experiments.